AI-POWERED PERSONALIZATION AND ITS IMPACT ON CONSUMER PURCHASE INTENT IN E-COMMERCE PLATFORMS

The present study investigated the impact of personalized recommendation systems on consumer purchase intention within e-commerce platforms. Using data collected from 1,000 online consumers, the study examined the relationships among recommendation accuracy, perceived relevance, personalization quality, trust, privacy concerns, perceived usefulness, customer satisfaction, purchase intention, and actual purchase behavior through Structural Equation Modeling, mediation analysis, logistic regression, and multi group analysis.  The descriptive findings indicated that respondents generally held moderate perceptions regarding recommendation accuracy, personalization quality, trust, usefulness, and satisfaction. The normality and multicollinearity assessments confirmed the suitability of the data for advanced statistical analysis. Furthermore, the structural model demonstrated excellent goodness-of-fit indices, indicating that the proposed conceptual framework adequately represented the observed data. Despite the satisfactory model fit, the empirical results revealed that none of the hypothesized relationships were statistically significant. Personalization quality did not significantly influence perceived relevance or trust. Similarly, trust, privacy concerns, perceived usefulness, and customer satisfaction did not significantly predict purchase intention. In addition, purchase intention failed to significantly influence actual purchase behavior. The mediation analysis further confirmed that perceived relevance did not mediate the relationship between personalization quality and trust. The low R-square values obtained across all endogenous constructs indicate that the variables included in the model explained only a small proportion of consumer purchase behavior. These findings suggest that consumers' purchasing decisions are influenced by factors beyond personalized recommendations. Variables such as product quality, pricing, brand reputation, perceived value, delivery reliability, prior experience, and promotional incentives may play a more substantial role in shaping online purchasing decisions. The study contributes to the literature on e-commerce consumer behavior by demonstrating that positive perceptions of personalization do not necessarily translate into stronger purchase intentions or actual purchasing behavior. The findings emphasize that personalization should be viewed as one component of a broader customer experience strategy rather than a standalone driver of purchasing decisions. In conclusion, while personalized recommendation systems may enhance user experience and improve consumer perceptions, they were not found to be significant determinants of purchase intention or actual purchase behavior in the present study.

1.        Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548 2.        Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 3.        Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 4.        Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (9th ed.). Cengage Learning. 5.        Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 6.        Kotler, P., Keller, K. L., & Chernev, A. (2023). Marketing management (16th ed.). Pearson. 7.        Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy and consumer engagement. Journal of Business Research, 123, 563–575. https://doi.org/10.1016/j.jbusres.2020.10.045 8.        Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2017). Explaining online shopping behavior with technology acceptance and flow theory. Information & Management, 54(6), 733–747. https://doi.org/10.1016/j.im.2016.11.001 9.        Schiffman, L. G., Wisenblit, J., & Kumar, S. R. (2022). Consumer behavior (13th ed.). Pearson. 10.     Shankar, V. (2023). How artificial intelligence is reshaping retailing. Journal of Retailing, 99(2), 219–230. https://doi.org/10.1016/j.jretai.2023.01.002